郭同 created SPARK-15404:
--------------------------
Summary: pyspark sql bug ,here is the testcase
Key: SPARK-15404
URL: https://issues.apache.org/jira/browse/SPARK-15404
Project: Spark
Issue Type: Bug
Environment: 1.6
Reporter: 郭同
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import os
import sys
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import Row, StructField, StructType, StringType,
IntegerType
if __name__ == "__main__":
sc = SparkContext(appName="PythonSQL")
sqlContext = SQLContext(sc)
schema = StructType([StructField("person_name", StringType(), False),
StructField("person_age", IntegerType(), False)])
# RDD is created from a list of rows
some_rdd = sc.parallelize([Row(person_name="John", person_age=19),
Row(person_name="Smith", person_age=23),
Row(person_name="Sarah", person_age=18)])
# Infer schema from the first row, create a DataFrame and print the schema
some_df = sqlContext.createDataFrame(some_rdd, schema)
some_df.printSchema()
# Another RDD is created from a list of tuples
another_rdd = sc.parallelize([("John", 19), ("Smith", 23), ("Sarah", 18)])
# Schema with two fields - person_name and person_age
# Create a DataFrame by applying the schema to the RDD and print the schema
another_df = sqlContext.createDataFrame(another_rdd, schema)
another_df.printSchema()
# print(some_df.filter(some_df.age > 20).collect())
# root
# |-- age: integer (nullable = true)
# |-- name: string (nullable = true)
# A JSON dataset is pointed to by path.
# The path can be either a single text file or a directory storing text
files.
if len(sys.argv) < 2:
path = "file://" + \
os.path.join(os.environ['SPARK_HOME'],
"examples/src/main/resources/people.json")
else:
path = sys.argv[1]
# Create a DataFrame from the file(s) pointed to by path
people = sqlContext.jsonFile(path)
# root
# |-- person_name: string (nullable = false)
# |-- person_age: integer (nullable = false)
# The inferred schema can be visualized using the printSchema() method.
people.printSchema()
# root
# |-- age: IntegerType
# |-- name: StringType
# Register this DataFrame as a table.
people.registerAsTable("people")
some_df.registerAsTable("people2")
# SQL statements can be run by using the sql methods provided by sqlContext
teenagers = sqlContext.sql("SELECT * FROM people2 ")
print("!!!!!---------------\n")
for each in teenagers.collect():
print(each)
print("---------------\n")
print("!!!!!-------------\n")
sc.stop()
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
